Short-term forecasting optimization algorithm for unsteady wind speed signal based on wavelet analysis method and neutral networks method

Hui Liu, Hong Qi Tian, Chao Chen, Yan Fei Li

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)


To promote the forecasting performance of traditional neural networks for non-stationary wind speed signal, an optimization algorithm was proposed based on wavelet analysis method and neural networks method. This optimization algorithm employed wavelet analysis method to make signal decomposition and reconstruction calculations for original wind speed series attain more steady sub-series. Then BP neural networks method was used to build unsteady prediction models for each layer to realize multi-step rolling forecast calculation. Simulation results show that the optimization algorithm can attain high-precision multi-step ahead forecast results, respectively improve forecast precision of traditional BP neural networks method by 55.56%, 32.43% and 34.58%, and the mean relative error of one-step, three-step and five-step ahead forecast are 0.48%, 1.50% and 2.97%. The optimization has signal decomposition and self-learning ability.

Original languageEnglish
Pages (from-to)2704-2711
Number of pages8
JournalJournal of Central South University (Science and Technology)
Issue number9
Publication statusPublished - Sep 2011


  • Neural networks method
  • Optimization algorithm
  • Wavelet analysis method
  • Wind speed forecast

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